Data mining and machine learning in textile industry

Abstract

Data mining has been proven useful for knowledge discovery in many areas, ranging from marketing to medical and from banking to education. This study focuses on data mining and machine learning in textile industry as applying them to textile data is considered an emerging interdisciplinary research field. Thus, data mining studies, including classification and clustering techniques and machine learning algorithms, implemented in textile industry were presented and explained in detail in this study to provide an overview of how clustering and classification techniques can be applied in the textile industry to deal with different problems where traditional methods are not useful. This article clearly shows that a classification technique has higher interest than a clustering technique in the textile industry. It also shows that the most commonly applied classification methods are artificial neural networks and support vector machines, and they generally provide high accuracy rates in the textile applications. For the clustering task of data mining, a K-means algorithm was generally implemented in textile studies among the others that were investigated in this article. We conclude with some remarks on the strength of the data mining techniques for textile industry, ways to overcome certain challenges, and offer some possible further research directions.

The mitigating effects of economic complexity and renewable energy on carbon emissions in developed countries

“…Thus, they should orient themselves to complex activities rather than traditional products. Complex economies combine large amounts of productive knowledge to generate sophisticated products, while simple economies can only make less diversified and straightforward products based on limited productive knowledge (Yildirim, Birant, & Alpyildiz, 2018).…”

Predicting Physical Properties of Woven Fabrics via Automated Machine Learning and Textile Design and Finishing Features

“…The methodology involves interactions between business domain and DM/ML experts and several iterations that can include up to six main phases: business understanding, data understanding, data preparation, modeling, evaluation and deployment. Regarding the textile domain, use of DM techniques is more recent, involving mainly classification tasks, such as defect detection and estimating the quality of yarns [26].…”

Predicting the Tear Strength of Woven Fabrics Via Automated Machine Learning: An Application of the CRISP-DM Methodology

“…DM techniques started being used in textile engineering during recent years, aiming to solve the difficulties of classical mathematical and statistics in modeling the complex relationships present in the data. Most DM applications to the textile industry involve classification tasks, such as qual-ity control (e.g., textile image inspection) (Yildirim et al, 2018). The application of DM to test areas is more scarce, in particular regarding the prediction of tear strength.…”

“…In particular, a large amount of data is created and stored, such as the properties of each yarn (e.g., color, thickness), the configuration of each machine used in the creation process (e.g., spinning, weaving) (Mozafary and Payvandy, 2014), and the results of the specific tests that the company executes. All these data can be processed by Data Mining (DM) and Machine Learning (ML) methods, allowing the discovery of valuable knowledge in order to improve the textile manufacturing process (Yildirim et al, 2018).…”